Overview

Dataset statistics

Number of variables34
Number of observations38478
Missing cells60589
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.0 MiB
Average record size in memory272.0 B

Variable types

Numeric19
Categorical8
Text7

Alerts

loan_amnt is highly overall correlated with funded_amnt and 6 other fieldsHigh correlation
funded_amnt is highly overall correlated with loan_amnt and 6 other fieldsHigh correlation
funded_amnt_inv is highly overall correlated with loan_amnt and 6 other fieldsHigh correlation
installment is highly overall correlated with loan_amnt and 6 other fieldsHigh correlation
delinq_2yrs is highly overall correlated with mths_since_last_delinqHigh correlation
mths_since_last_delinq is highly overall correlated with delinq_2yrsHigh correlation
open_acc is highly overall correlated with total_accHigh correlation
total_acc is highly overall correlated with open_accHigh correlation
total_pymnt is highly overall correlated with loan_amnt and 6 other fieldsHigh correlation
total_pymnt_inv is highly overall correlated with loan_amnt and 6 other fieldsHigh correlation
total_rec_prncp is highly overall correlated with loan_amnt and 7 other fieldsHigh correlation
total_rec_int is highly overall correlated with loan_amnt and 7 other fieldsHigh correlation
last_pymnt_amnt is highly overall correlated with total_rec_prncpHigh correlation
term is highly overall correlated with total_rec_intHigh correlation
loan_status is highly overall correlated with repay_failHigh correlation
repay_fail is highly overall correlated with loan_statusHigh correlation
loan_status is highly imbalanced (64.9%)Imbalance
emp_length has 993 (2.6%) missing valuesMissing
mths_since_last_delinq has 24362 (63.3%) missing valuesMissing
next_pymnt_d has 35097 (91.2%) missing valuesMissing
annual_inc is highly skewed (γ1 = 30.55623967)Skewed
delinq_2yrs has 34200 (88.9%) zerosZeros
inq_last_6mths has 17787 (46.2%) zerosZeros
mths_since_last_delinq has 745 (1.9%) zerosZeros
pub_rec has 36339 (94.4%) zerosZeros
revol_bal has 976 (2.5%) zerosZeros

Reproduction

Analysis started2023-08-27 15:41:59.812498
Analysis finished2023-08-27 15:43:36.516996
Duration1 minute and 36.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

loan_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct880
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11095.016
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:36.699736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15200
median9750
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9800

Descriptive statistics

Standard deviation7405.2963
Coefficient of variation (CV)0.6674435
Kurtosis0.78703609
Mean11095.016
Median Absolute Deviation (MAD)4750
Skewness1.0641972
Sum4.2691402 × 108
Variance54838413
MonotonicityNot monotonic
2023-08-27T18:43:36.958996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2727
 
7.1%
12000 2235
 
5.8%
5000 2027
 
5.3%
6000 1842
 
4.8%
15000 1823
 
4.7%
20000 1575
 
4.1%
8000 1550
 
4.0%
25000 1348
 
3.5%
4000 1111
 
2.9%
3000 1013
 
2.6%
Other values (870) 21227
55.2%
ValueCountFrequency (%)
500 10
< 0.1%
550 1
 
< 0.1%
600 5
< 0.1%
700 3
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 2
 
< 0.1%
850 1
 
< 0.1%
900 4
 
< 0.1%
925 1
 
< 0.1%
ValueCountFrequency (%)
35000 618
1.6%
34800 2
 
< 0.1%
34675 1
 
< 0.1%
34525 1
 
< 0.1%
34475 5
 
< 0.1%
34000 15
 
< 0.1%
33950 8
 
< 0.1%
33600 5
 
< 0.1%
33500 2
 
< 0.1%
33425 2
 
< 0.1%

funded_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct1035
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10832.138
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:37.243096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2296.25
Q15100
median9600
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9900

Descriptive statistics

Standard deviation7146.7332
Coefficient of variation (CV)0.65977126
Kurtosis0.94291679
Mean10832.138
Median Absolute Deviation (MAD)4600
Skewness1.0838181
Sum4.16799 × 108
Variance51075796
MonotonicityNot monotonic
2023-08-27T18:43:37.492189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2642
 
6.9%
12000 2156
 
5.6%
5000 2015
 
5.2%
6000 1829
 
4.8%
15000 1717
 
4.5%
8000 1542
 
4.0%
20000 1417
 
3.7%
4000 1111
 
2.9%
25000 1103
 
2.9%
3000 1004
 
2.6%
Other values (1025) 21942
57.0%
ValueCountFrequency (%)
500 10
< 0.1%
550 1
 
< 0.1%
600 5
< 0.1%
700 3
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 2
 
< 0.1%
850 1
 
< 0.1%
900 4
 
< 0.1%
925 1
 
< 0.1%
ValueCountFrequency (%)
35000 505
1.3%
34800 1
 
< 0.1%
34675 2
 
< 0.1%
34525 1
 
< 0.1%
34475 4
 
< 0.1%
34250 1
 
< 0.1%
34000 14
 
< 0.1%
33950 5
 
< 0.1%
33600 5
 
< 0.1%
33500 1
 
< 0.1%

funded_amnt_inv
Real number (ℝ)

HIGH CORRELATION 

Distinct8473
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10150.405
Minimum0
Maximum35000
Zeros200
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:37.762753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1500
Q14950
median8497.6108
Q314000
95-th percentile24561.801
Maximum35000
Range35000
Interquartile range (IQR)9050

Descriptive statistics

Standard deviation7127.9316
Coefficient of variation (CV)0.70223123
Kurtosis1.0670681
Mean10150.405
Median Absolute Deviation (MAD)4197.6108
Skewness1.1047838
Sum3.905673 × 108
Variance50807409
MonotonicityNot monotonic
2023-08-27T18:43:38.021126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 1232
 
3.2%
10000 1174
 
3.1%
6000 1116
 
2.9%
12000 980
 
2.5%
8000 848
 
2.2%
3000 762
 
2.0%
4000 761
 
2.0%
15000 593
 
1.5%
7000 566
 
1.5%
2000 443
 
1.2%
Other values (8463) 30003
78.0%
ValueCountFrequency (%)
0 200
0.5%
0.000121098 1
 
< 0.1%
0.000185369 1
 
< 0.1%
0.000242056 1
 
< 0.1%
0.000531133 1
 
< 0.1%
0.000571783 1
 
< 0.1%
0.000654607 1
 
< 0.1%
0.000899697 1
 
< 0.1%
0.00092203 1
 
< 0.1%
0.00108361 1
 
< 0.1%
ValueCountFrequency (%)
35000 123
0.3%
34997.35245 1
 
< 0.1%
34993.65539 1
 
< 0.1%
34993.26306 1
 
< 0.1%
34993.19696 1
 
< 0.1%
34990.4308 1
 
< 0.1%
34987.98452 1
 
< 0.1%
34987.27101 1
 
< 0.1%
34977.34674 1
 
< 0.1%
34975.81636 1
 
< 0.1%

term
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
36 months
28592 
60 months
9886 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters346302
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36 months
2nd row36 months
3rd row36 months
4th row36 months
5th row36 months

Common Values

ValueCountFrequency (%)
36 months 28592
74.3%
60 months 9886
 
25.7%

Length

2023-08-27T18:43:38.261723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-27T18:43:38.504239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
months 38478
50.0%
36 28592
37.2%
60 9886
 
12.8%

Most occurring characters

ValueCountFrequency (%)
6 38478
11.1%
38478
11.1%
m 38478
11.1%
o 38478
11.1%
n 38478
11.1%
t 38478
11.1%
h 38478
11.1%
s 38478
11.1%
3 28592
8.3%
0 9886
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 230868
66.7%
Decimal Number 76956
 
22.2%
Space Separator 38478
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 38478
16.7%
o 38478
16.7%
n 38478
16.7%
t 38478
16.7%
h 38478
16.7%
s 38478
16.7%
Decimal Number
ValueCountFrequency (%)
6 38478
50.0%
3 28592
37.2%
0 9886
 
12.8%
Space Separator
ValueCountFrequency (%)
38478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 230868
66.7%
Common 115434
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 38478
16.7%
o 38478
16.7%
n 38478
16.7%
t 38478
16.7%
h 38478
16.7%
s 38478
16.7%
Common
ValueCountFrequency (%)
6 38478
33.3%
38478
33.3%
3 28592
24.8%
0 9886
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 346302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 38478
11.1%
38478
11.1%
m 38478
11.1%
o 38478
11.1%
n 38478
11.1%
t 38478
11.1%
h 38478
11.1%
s 38478
11.1%
3 28592
8.3%
0 9886
 
2.9%

int_rate
Real number (ℝ)

Distinct390
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.162307
Minimum5.42
Maximum24.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:38.732774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.39
Q19.62
median11.99
Q314.72
95-th percentile18.61
Maximum24.11
Range18.69
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation3.7094849
Coefficient of variation (CV)0.30499845
Kurtosis-0.47598526
Mean12.162307
Median Absolute Deviation (MAD)2.66
Skewness0.23462005
Sum467981.26
Variance13.760278
MonotonicityNot monotonic
2023-08-27T18:43:38.995317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.99 861
 
2.2%
11.49 759
 
2.0%
13.49 752
 
2.0%
7.51 704
 
1.8%
7.88 675
 
1.8%
7.49 593
 
1.5%
11.71 545
 
1.4%
7.9 529
 
1.4%
9.99 528
 
1.4%
5.42 525
 
1.4%
Other values (380) 32007
83.2%
ValueCountFrequency (%)
5.42 525
1.4%
5.79 390
1.0%
5.99 311
0.8%
6 19
 
< 0.1%
6.03 412
1.1%
6.17 225
0.6%
6.39 52
 
0.1%
6.54 278
0.7%
6.62 369
1.0%
6.76 150
 
0.4%
ValueCountFrequency (%)
24.11 3
 
< 0.1%
23.91 11
< 0.1%
23.59 4
 
< 0.1%
23.52 8
< 0.1%
23.22 9
< 0.1%
23.13 9
< 0.1%
22.94 2
 
< 0.1%
22.85 7
 
< 0.1%
22.74 19
< 0.1%
22.64 1
 
< 0.1%

installment
Real number (ℝ)

HIGH CORRELATION 

Distinct15474
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.17165
Minimum15.67
Maximum1305.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:39.283520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum15.67
5-th percentile69.7425
Q1165.74
median277.98
Q3429.35
95-th percentile761.944
Maximum1305.19
Range1289.52
Interquartile range (IQR)263.61

Descriptive statistics

Standard deviation209.08532
Coefficient of variation (CV)0.64697915
Kurtosis1.2029335
Mean323.17165
Median Absolute Deviation (MAD)122.42
Skewness1.1233356
Sum12434999
Variance43716.672
MonotonicityNot monotonic
2023-08-27T18:43:39.563732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.11 65
 
0.2%
180.96 55
 
0.1%
311.02 47
 
0.1%
368.45 43
 
0.1%
150.8 42
 
0.1%
372.12 42
 
0.1%
373.33 39
 
0.1%
186.61 39
 
0.1%
317.72 39
 
0.1%
304.36 38
 
0.1%
Other values (15464) 38029
98.8%
ValueCountFrequency (%)
15.67 1
< 0.1%
15.69 1
< 0.1%
15.75 1
< 0.1%
15.76 1
< 0.1%
15.91 1
< 0.1%
16.08 1
< 0.1%
16.31 1
< 0.1%
16.47 1
< 0.1%
16.73 1
< 0.1%
16.85 1
< 0.1%
ValueCountFrequency (%)
1305.19 1
 
< 0.1%
1302.69 1
 
< 0.1%
1295.21 1
 
< 0.1%
1288.1 2
 
< 0.1%
1283.5 1
 
< 0.1%
1276.6 3
< 0.1%
1272.2 1
 
< 0.1%
1269.73 5
< 0.1%
1265.16 1
 
< 0.1%
1263.23 1
 
< 0.1%

emp_length
Categorical

MISSING 

Distinct11
Distinct (%)< 0.1%
Missing993
Missing (%)2.6%
Memory size300.7 KiB
10+ years
8464 
< 1 year
4564 
2 years
4292 
3 years
3939 
4 years
3314 
Other values (6)
12912 

Length

Max length9
Median length7
Mean length7.4865413
Min length6

Characters and Unicode

Total characters280633
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4 years
2nd row4 years
3rd row10+ years
4th row10+ years
5th row10+ years

Common Values

ValueCountFrequency (%)
10+ years 8464
22.0%
< 1 year 4564
11.9%
2 years 4292
11.2%
3 years 3939
10.2%
4 years 3314
 
8.6%
1 year 3254
 
8.5%
5 years 3171
 
8.2%
6 years 2144
 
5.6%
7 years 1702
 
4.4%
8 years 1445
 
3.8%

Length

2023-08-27T18:43:39.801363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 29667
37.3%
10 8464
 
10.6%
1 7818
 
9.8%
year 7818
 
9.8%
4564
 
5.7%
2 4292
 
5.4%
3 3939
 
5.0%
4 3314
 
4.2%
5 3171
 
4.0%
6 2144
 
2.7%
Other values (3) 4343
 
5.5%

Most occurring characters

ValueCountFrequency (%)
42049
15.0%
y 37485
13.4%
e 37485
13.4%
a 37485
13.4%
r 37485
13.4%
s 29667
10.6%
1 16282
 
5.8%
0 8464
 
3.0%
+ 8464
 
3.0%
< 4564
 
1.6%
Other values (8) 21203
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 179607
64.0%
Decimal Number 45949
 
16.4%
Space Separator 42049
 
15.0%
Math Symbol 13028
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16282
35.4%
0 8464
18.4%
2 4292
 
9.3%
3 3939
 
8.6%
4 3314
 
7.2%
5 3171
 
6.9%
6 2144
 
4.7%
7 1702
 
3.7%
8 1445
 
3.1%
9 1196
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
y 37485
20.9%
e 37485
20.9%
a 37485
20.9%
r 37485
20.9%
s 29667
16.5%
Math Symbol
ValueCountFrequency (%)
+ 8464
65.0%
< 4564
35.0%
Space Separator
ValueCountFrequency (%)
42049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 179607
64.0%
Common 101026
36.0%

Most frequent character per script

Common
ValueCountFrequency (%)
42049
41.6%
1 16282
 
16.1%
0 8464
 
8.4%
+ 8464
 
8.4%
< 4564
 
4.5%
2 4292
 
4.2%
3 3939
 
3.9%
4 3314
 
3.3%
5 3171
 
3.1%
6 2144
 
2.1%
Other values (3) 4343
 
4.3%
Latin
ValueCountFrequency (%)
y 37485
20.9%
e 37485
20.9%
a 37485
20.9%
r 37485
20.9%
s 29667
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 280633
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42049
15.0%
y 37485
13.4%
e 37485
13.4%
a 37485
13.4%
r 37485
13.4%
s 29667
10.6%
1 16282
 
5.8%
0 8464
 
3.0%
+ 8464
 
3.0%
< 4564
 
1.6%
Other values (8) 21203
7.6%

home_ownership
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
RENT
18252 
MORTGAGE
17139 
OWN
2958 
OTHER
 
125
NONE
 
4

Length

Max length8
Median length5
Mean length5.7080669
Min length3

Characters and Unicode

Total characters219635
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowMORTGAGE
4th rowRENT
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
RENT 18252
47.4%
MORTGAGE 17139
44.5%
OWN 2958
 
7.7%
OTHER 125
 
0.3%
NONE 4
 
< 0.1%

Length

2023-08-27T18:43:40.031351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-27T18:43:40.278753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rent 18252
47.4%
mortgage 17139
44.5%
own 2958
 
7.7%
other 125
 
0.3%
none 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 35520
16.2%
R 35516
16.2%
T 35516
16.2%
G 34278
15.6%
N 21218
9.7%
O 20226
9.2%
M 17139
7.8%
A 17139
7.8%
W 2958
 
1.3%
H 125
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 219635
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 35520
16.2%
R 35516
16.2%
T 35516
16.2%
G 34278
15.6%
N 21218
9.7%
O 20226
9.2%
M 17139
7.8%
A 17139
7.8%
W 2958
 
1.3%
H 125
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 219635
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 35520
16.2%
R 35516
16.2%
T 35516
16.2%
G 34278
15.6%
N 21218
9.7%
O 20226
9.2%
M 17139
7.8%
A 17139
7.8%
W 2958
 
1.3%
H 125
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219635
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 35520
16.2%
R 35516
16.2%
T 35516
16.2%
G 34278
15.6%
N 21218
9.7%
O 20226
9.2%
M 17139
7.8%
A 17139
7.8%
W 2958
 
1.3%
H 125
 
0.1%

annual_inc
Real number (ℝ)

SKEWED 

Distinct5190
Distinct (%)13.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean68997.102
Minimum1896
Maximum6000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:40.515342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1896
5-th percentile24000
Q140000
median58650
Q382000
95-th percentile144000
Maximum6000000
Range5998104
Interquartile range (IQR)42000

Descriptive statistics

Standard deviation64476.267
Coefficient of variation (CV)0.9344779
Kurtosis2264.8897
Mean68997.102
Median Absolute Deviation (MAD)19922.88
Skewness30.55624
Sum2.6548015 × 109
Variance4.157189 × 109
MonotonicityNot monotonic
2023-08-27T18:43:40.767593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 1440
 
3.7%
50000 1018
 
2.6%
40000 857
 
2.2%
45000 830
 
2.2%
30000 810
 
2.1%
75000 781
 
2.0%
65000 760
 
2.0%
70000 726
 
1.9%
48000 693
 
1.8%
80000 644
 
1.7%
Other values (5180) 29918
77.8%
ValueCountFrequency (%)
1896 1
 
< 0.1%
2000 1
 
< 0.1%
3300 1
 
< 0.1%
3500 1
 
< 0.1%
3600 1
 
< 0.1%
4000 3
< 0.1%
4080 1
 
< 0.1%
4200 2
 
< 0.1%
4800 5
< 0.1%
4888 1
 
< 0.1%
ValueCountFrequency (%)
6000000 1
 
< 0.1%
3900000 1
 
< 0.1%
2039784 1
 
< 0.1%
1782000 1
 
< 0.1%
1440000 2
< 0.1%
1362000 1
 
< 0.1%
1250000 1
 
< 0.1%
1200000 3
< 0.1%
1176000 1
 
< 0.1%
1080000 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
Not Verified
16961 
Verified
12169 
Source Verified
9348 

Length

Max length15
Median length12
Mean length11.463797
Min length8

Characters and Unicode

Total characters441104
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowNot Verified
3rd rowNot Verified
4th rowNot Verified
5th rowNot Verified

Common Values

ValueCountFrequency (%)
Not Verified 16961
44.1%
Verified 12169
31.6%
Source Verified 9348
24.3%

Length

2023-08-27T18:43:41.034227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-27T18:43:41.284228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
verified 38478
59.4%
not 16961
26.2%
source 9348
 
14.4%

Most occurring characters

ValueCountFrequency (%)
e 86304
19.6%
i 76956
17.4%
r 47826
10.8%
V 38478
8.7%
f 38478
8.7%
d 38478
8.7%
o 26309
 
6.0%
26309
 
6.0%
N 16961
 
3.8%
t 16961
 
3.8%
Other values (3) 28044
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 350008
79.3%
Uppercase Letter 64787
 
14.7%
Space Separator 26309
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 86304
24.7%
i 76956
22.0%
r 47826
13.7%
f 38478
11.0%
d 38478
11.0%
o 26309
 
7.5%
t 16961
 
4.8%
u 9348
 
2.7%
c 9348
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
V 38478
59.4%
N 16961
26.2%
S 9348
 
14.4%
Space Separator
ValueCountFrequency (%)
26309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 414795
94.0%
Common 26309
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 86304
20.8%
i 76956
18.6%
r 47826
11.5%
V 38478
9.3%
f 38478
9.3%
d 38478
9.3%
o 26309
 
6.3%
N 16961
 
4.1%
t 16961
 
4.1%
S 9348
 
2.3%
Other values (2) 18696
 
4.5%
Common
ValueCountFrequency (%)
26309
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 86304
19.6%
i 76956
17.4%
r 47826
10.8%
V 38478
8.7%
f 38478
8.7%
d 38478
8.7%
o 26309
 
6.0%
26309
 
6.0%
N 16961
 
3.8%
t 16961
 
3.8%
Other values (3) 28044
 
6.4%
Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:41.739073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters230868
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJul-10
2nd rowJun-10
3rd rowSep-11
4th rowSep-11
5th rowApr-10
ValueCountFrequency (%)
nov-11 2068
 
5.4%
dec-11 2056
 
5.3%
oct-11 1922
 
5.0%
sep-11 1852
 
4.8%
aug-11 1745
 
4.5%
jul-11 1672
 
4.3%
jun-11 1644
 
4.3%
may-11 1544
 
4.0%
apr-11 1405
 
3.7%
mar-11 1300
 
3.4%
Other values (45) 21270
55.3%
2023-08-27T18:43:42.287557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 50640
21.9%
- 38478
16.7%
0 18831
 
8.2%
e 10073
 
4.4%
u 9822
 
4.3%
J 8839
 
3.8%
c 8044
 
3.5%
a 7954
 
3.4%
p 6249
 
2.7%
A 6134
 
2.7%
Other values (18) 65804
28.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76956
33.3%
Lowercase Letter 76956
33.3%
Dash Punctuation 38478
16.7%
Uppercase Letter 38478
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10073
13.1%
u 9822
12.8%
c 8044
10.5%
a 7954
10.3%
p 6249
8.1%
n 5503
7.2%
r 5479
7.1%
o 4052
 
5.3%
v 4052
 
5.3%
t 3804
 
4.9%
Other values (4) 11924
15.5%
Uppercase Letter
ValueCountFrequency (%)
J 8839
23.0%
A 6134
15.9%
M 5576
14.5%
D 4240
11.0%
N 4052
10.5%
O 3804
9.9%
S 3476
 
9.0%
F 2357
 
6.1%
Decimal Number
ValueCountFrequency (%)
1 50640
65.8%
0 18831
 
24.5%
9 4804
 
6.2%
8 2167
 
2.8%
7 514
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 38478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115434
50.0%
Latin 115434
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10073
 
8.7%
u 9822
 
8.5%
J 8839
 
7.7%
c 8044
 
7.0%
a 7954
 
6.9%
p 6249
 
5.4%
A 6134
 
5.3%
M 5576
 
4.8%
n 5503
 
4.8%
r 5479
 
4.7%
Other values (12) 41761
36.2%
Common
ValueCountFrequency (%)
1 50640
43.9%
- 38478
33.3%
0 18831
 
16.3%
9 4804
 
4.2%
8 2167
 
1.9%
7 514
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 50640
21.9%
- 38478
16.7%
0 18831
 
8.2%
e 10073
 
4.4%
u 9822
 
4.3%
J 8839
 
3.8%
c 8044
 
3.5%
a 7954
 
3.4%
p 6249
 
2.7%
A 6134
 
2.7%
Other values (18) 65804
28.5%

loan_status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
Fully Paid
29987 
Charged Off
5110 
Does not meet the credit policy. Status:Fully Paid
 
1782
Current
 
866
Does not meet the credit policy. Status:Charged Off
 
689
Other values (4)
 
44

Length

Max length51
Median length10
Mean length12.659156
Min length7

Characters and Unicode

Total characters487099
Distinct characters38
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDoes not meet the credit policy. Status:Fully Paid
2nd rowCharged Off
3rd rowFully Paid
4th rowFully Paid
5th rowDoes not meet the credit policy. Status:Fully Paid

Common Values

ValueCountFrequency (%)
Fully Paid 29987
77.9%
Charged Off 5110
 
13.3%
Does not meet the credit policy. Status:Fully Paid 1782
 
4.6%
Current 866
 
2.3%
Does not meet the credit policy. Status:Charged Off 689
 
1.8%
Late (31-120 days) 20
 
0.1%
In Grace Period 15
 
< 0.1%
Late (16-30 days) 7
 
< 0.1%
Default 2
 
< 0.1%

Length

2023-08-27T18:43:42.586478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-27T18:43:42.854934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
paid 31769
34.9%
fully 29987
33.0%
off 5799
 
6.4%
charged 5110
 
5.6%
does 2471
 
2.7%
not 2471
 
2.7%
meet 2471
 
2.7%
the 2471
 
2.7%
credit 2471
 
2.7%
policy 2471
 
2.7%
Other values (11) 3465
 
3.8%

Most occurring characters

ValueCountFrequency (%)
l 66011
13.6%
52478
10.8%
a 40110
8.2%
d 40081
8.2%
i 36726
 
7.5%
u 35108
 
7.2%
y 34267
 
7.0%
P 31784
 
6.5%
F 31769
 
6.5%
e 19079
 
3.9%
Other values (28) 99686
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 348452
71.5%
Uppercase Letter 81018
 
16.6%
Space Separator 52478
 
10.8%
Other Punctuation 4942
 
1.0%
Decimal Number 128
 
< 0.1%
Dash Punctuation 27
 
< 0.1%
Close Punctuation 27
 
< 0.1%
Open Punctuation 27
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 66011
18.9%
a 40110
11.5%
d 40081
11.5%
i 36726
10.5%
u 35108
10.1%
y 34267
9.8%
e 19079
 
5.5%
t 15721
 
4.5%
f 11600
 
3.3%
r 10032
 
2.9%
Other values (8) 39717
11.4%
Uppercase Letter
ValueCountFrequency (%)
P 31784
39.2%
F 31769
39.2%
C 6665
 
8.2%
O 5799
 
7.2%
D 2473
 
3.1%
S 2471
 
3.0%
L 27
 
< 0.1%
I 15
 
< 0.1%
G 15
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 47
36.7%
0 27
21.1%
3 27
21.1%
2 20
15.6%
6 7
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 2471
50.0%
: 2471
50.0%
Space Separator
ValueCountFrequency (%)
52478
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 429470
88.2%
Common 57629
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 66011
15.4%
a 40110
9.3%
d 40081
9.3%
i 36726
8.6%
u 35108
8.2%
y 34267
8.0%
P 31784
7.4%
F 31769
7.4%
e 19079
 
4.4%
t 15721
 
3.7%
Other values (17) 78814
18.4%
Common
ValueCountFrequency (%)
52478
91.1%
. 2471
 
4.3%
: 2471
 
4.3%
1 47
 
0.1%
- 27
 
< 0.1%
) 27
 
< 0.1%
0 27
 
< 0.1%
3 27
 
< 0.1%
( 27
 
< 0.1%
2 20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 487099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 66011
13.6%
52478
10.8%
a 40110
8.2%
d 40081
8.2%
i 36726
 
7.5%
u 35108
 
7.2%
y 34267
 
7.0%
P 31784
 
6.5%
F 31769
 
6.5%
e 19079
 
3.9%
Other values (28) 99686
20.5%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
debt_consolidation
17917 
credit_card
4973 
other
3950 
home_improvement
2901 
major_purchase
2078 
Other values (9)
6659 

Length

Max length18
Median length16
Mean length13.70131
Min length3

Characters and Unicode

Total characters527199
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother
2nd rowdebt_consolidation
3rd rowother
4th rowdebt_consolidation
5th rowother

Common Values

ValueCountFrequency (%)
debt_consolidation 17917
46.6%
credit_card 4973
 
12.9%
other 3950
 
10.3%
home_improvement 2901
 
7.5%
major_purchase 2078
 
5.4%
small_business 1808
 
4.7%
car 1481
 
3.8%
wedding 909
 
2.4%
medical 675
 
1.8%
moving 562
 
1.5%
Other values (4) 1224
 
3.2%

Length

2023-08-27T18:43:43.149606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 17917
46.6%
credit_card 4973
 
12.9%
other 3950
 
10.3%
home_improvement 2901
 
7.5%
major_purchase 2078
 
5.4%
small_business 1808
 
4.7%
car 1481
 
3.8%
wedding 909
 
2.4%
medical 675
 
1.8%
moving 562
 
1.5%
Other values (4) 1224
 
3.2%

Most occurring characters

ValueCountFrequency (%)
o 67276
12.8%
d 48659
9.2%
i 48408
9.2%
t 48404
9.2%
n 42942
8.1%
e 42241
 
8.0%
c 32843
 
6.2%
a 32593
 
6.2%
_ 29768
 
5.6%
s 27614
 
5.2%
Other values (12) 106451
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 497431
94.4%
Connector Punctuation 29768
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 67276
13.5%
d 48659
9.8%
i 48408
9.7%
t 48404
9.7%
n 42942
8.6%
e 42241
8.5%
c 32843
 
6.6%
a 32593
 
6.6%
s 27614
 
5.6%
l 22685
 
4.6%
Other values (11) 83766
16.8%
Connector Punctuation
ValueCountFrequency (%)
_ 29768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 497431
94.4%
Common 29768
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 67276
13.5%
d 48659
9.8%
i 48408
9.7%
t 48404
9.7%
n 42942
8.6%
e 42241
8.5%
c 32843
 
6.6%
a 32593
 
6.6%
s 27614
 
5.6%
l 22685
 
4.6%
Other values (11) 83766
16.8%
Common
ValueCountFrequency (%)
_ 29768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 527199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 67276
12.8%
d 48659
9.2%
i 48408
9.2%
t 48404
9.2%
n 42942
8.1%
e 42241
 
8.0%
c 32843
 
6.2%
a 32593
 
6.2%
_ 29768
 
5.6%
s 27614
 
5.2%
Other values (12) 106451
20.2%
Distinct832
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:43.668776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters192390
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)0.1%

Sample

1st row487xx
2nd row115xx
3rd row751xx
4th row112xx
5th row352xx
ValueCountFrequency (%)
100xx 573
 
1.5%
945xx 502
 
1.3%
606xx 499
 
1.3%
112xx 492
 
1.3%
070xx 447
 
1.2%
900xx 438
 
1.1%
300xx 395
 
1.0%
021xx 375
 
1.0%
750xx 362
 
0.9%
926xx 354
 
0.9%
Other values (822) 34041
88.5%
2023-08-27T18:43:44.421018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
x 76956
40.0%
0 19159
 
10.0%
1 15128
 
7.9%
2 13067
 
6.8%
9 12179
 
6.3%
3 12075
 
6.3%
7 9870
 
5.1%
4 8824
 
4.6%
5 8804
 
4.6%
8 8445
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 115434
60.0%
Lowercase Letter 76956
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19159
16.6%
1 15128
13.1%
2 13067
11.3%
9 12179
10.6%
3 12075
10.5%
7 9870
8.6%
4 8824
7.6%
5 8804
7.6%
8 8445
7.3%
6 7883
6.8%
Lowercase Letter
ValueCountFrequency (%)
x 76956
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115434
60.0%
Latin 76956
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19159
16.6%
1 15128
13.1%
2 13067
11.3%
9 12179
10.6%
3 12075
10.5%
7 9870
8.6%
4 8824
7.6%
5 8804
7.6%
8 8445
7.3%
6 7883
6.8%
Latin
ValueCountFrequency (%)
x 76956
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x 76956
40.0%
0 19159
 
10.0%
1 15128
 
7.9%
2 13067
 
6.8%
9 12179
 
6.3%
3 12075
 
6.3%
7 9870
 
5.1%
4 8824
 
4.6%
5 8804
 
4.6%
8 8445
 
4.4%

addr_state
Categorical

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
CA
6754 
NY
3702 
FL
2803 
TX
2633 
NJ
 
1795
Other values (45)
20791 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters76956
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMI
2nd rowNY
3rd rowTX
4th rowNY
5th rowAL

Common Values

ValueCountFrequency (%)
CA 6754
17.6%
NY 3702
 
9.6%
FL 2803
 
7.3%
TX 2633
 
6.8%
NJ 1795
 
4.7%
IL 1523
 
4.0%
PA 1491
 
3.9%
GA 1371
 
3.6%
VA 1343
 
3.5%
MA 1285
 
3.3%
Other values (40) 13778
35.8%

Length

2023-08-27T18:43:44.719854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 6754
17.6%
ny 3702
 
9.6%
fl 2803
 
7.3%
tx 2633
 
6.8%
nj 1795
 
4.7%
il 1523
 
4.0%
pa 1491
 
3.9%
ga 1371
 
3.6%
va 1343
 
3.5%
ma 1285
 
3.3%
Other values (40) 13778
35.8%

Most occurring characters

ValueCountFrequency (%)
A 15079
19.6%
C 9628
12.5%
N 7741
10.1%
L 5180
 
6.7%
M 4578
 
5.9%
Y 4093
 
5.3%
T 3779
 
4.9%
O 3360
 
4.4%
I 3097
 
4.0%
F 2803
 
3.6%
Other values (14) 17618
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 76956
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15079
19.6%
C 9628
12.5%
N 7741
10.1%
L 5180
 
6.7%
M 4578
 
5.9%
Y 4093
 
5.3%
T 3779
 
4.9%
O 3360
 
4.4%
I 3097
 
4.0%
F 2803
 
3.6%
Other values (14) 17618
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 76956
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15079
19.6%
C 9628
12.5%
N 7741
10.1%
L 5180
 
6.7%
M 4578
 
5.9%
Y 4093
 
5.3%
T 3779
 
4.9%
O 3360
 
4.4%
I 3097
 
4.0%
F 2803
 
3.6%
Other values (14) 17618
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15079
19.6%
C 9628
12.5%
N 7741
10.1%
L 5180
 
6.7%
M 4578
 
5.9%
Y 4093
 
5.3%
T 3779
 
4.9%
O 3360
 
4.4%
I 3097
 
4.0%
F 2803
 
3.6%
Other values (14) 17618
22.9%

dti
Real number (ℝ)

Distinct2877
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.376215
Minimum0
Maximum29.99
Zeros190
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:44.938604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.1
Q18.2
median13.485
Q318.69
95-th percentile23.92
Maximum29.99
Range29.99
Interquartile range (IQR)10.49

Descriptive statistics

Standard deviation6.7297127
Coefficient of variation (CV)0.50311038
Kurtosis-0.85284363
Mean13.376215
Median Absolute Deviation (MAD)5.235
Skewness-0.0310279
Sum514690.01
Variance45.289033
MonotonicityNot monotonic
2023-08-27T18:43:45.196543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 190
 
0.5%
12 44
 
0.1%
18 42
 
0.1%
16.8 39
 
0.1%
19.2 39
 
0.1%
13.2 39
 
0.1%
12.48 38
 
0.1%
4.8 35
 
0.1%
15 35
 
0.1%
13.5 34
 
0.1%
Other values (2867) 37943
98.6%
ValueCountFrequency (%)
0 190
0.5%
0.01 3
 
< 0.1%
0.02 5
 
< 0.1%
0.03 2
 
< 0.1%
0.04 3
 
< 0.1%
0.05 2
 
< 0.1%
0.06 1
 
< 0.1%
0.07 4
 
< 0.1%
0.08 5
 
< 0.1%
0.09 4
 
< 0.1%
ValueCountFrequency (%)
29.99 1
 
< 0.1%
29.96 1
 
< 0.1%
29.95 1
 
< 0.1%
29.93 3
< 0.1%
29.92 2
< 0.1%
29.89 1
 
< 0.1%
29.88 1
 
< 0.1%
29.86 2
< 0.1%
29.85 1
 
< 0.1%
29.83 1
 
< 0.1%

delinq_2yrs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15193097
Minimum0
Maximum11
Zeros34200
Zeros (%)88.9%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:45.451360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5075978
Coefficient of variation (CV)3.3409764
Kurtosis42.56804
Mean0.15193097
Median Absolute Deviation (MAD)0
Skewness5.1435969
Sum5846
Variance0.25765552
MonotonicityNot monotonic
2023-08-27T18:43:45.648030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 34200
88.9%
1 3244
 
8.4%
2 700
 
1.8%
3 224
 
0.6%
4 62
 
0.2%
5 26
 
0.1%
6 13
 
< 0.1%
7 5
 
< 0.1%
11 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 34200
88.9%
1 3244
 
8.4%
2 700
 
1.8%
3 224
 
0.6%
4 62
 
0.2%
5 26
 
0.1%
6 13
 
< 0.1%
7 5
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 5
 
< 0.1%
6 13
 
< 0.1%
5 26
 
0.1%
4 62
 
0.2%
3 224
 
0.6%
2 700
 
1.8%
1 3244
8.4%
Distinct528
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:46.098004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters230868
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.1%

Sample

1st rowAug-05
2nd rowApr-94
3rd rowMar-98
4th rowJan-75
5th rowApr-98
ValueCountFrequency (%)
nov-98 352
 
0.9%
oct-99 351
 
0.9%
oct-00 339
 
0.9%
dec-98 326
 
0.8%
nov-99 314
 
0.8%
nov-00 311
 
0.8%
dec-97 305
 
0.8%
sep-00 301
 
0.8%
oct-98 296
 
0.8%
nov-97 288
 
0.7%
Other values (518) 35295
91.7%
2023-08-27T18:43:46.791982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 38478
16.7%
9 22596
 
9.8%
0 18766
 
8.1%
e 10137
 
4.4%
J 9143
 
4.0%
u 8997
 
3.9%
a 8870
 
3.8%
8 8151
 
3.5%
c 7826
 
3.4%
p 6192
 
2.7%
Other values (23) 91712
39.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76956
33.3%
Lowercase Letter 76956
33.3%
Dash Punctuation 38478
16.7%
Uppercase Letter 38478
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10137
13.2%
u 8997
11.7%
a 8870
11.5%
c 7826
10.2%
p 6192
8.0%
n 6164
8.0%
r 5411
7.0%
t 3961
 
5.1%
o 3815
 
5.0%
v 3815
 
5.0%
Other values (4) 11768
15.3%
Decimal Number
ValueCountFrequency (%)
9 22596
29.4%
0 18766
24.4%
8 8151
 
10.6%
7 4652
 
6.0%
4 4127
 
5.4%
6 4124
 
5.4%
5 4087
 
5.3%
3 3656
 
4.8%
1 3610
 
4.7%
2 3187
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
J 9143
23.8%
A 5889
15.3%
M 5533
14.4%
O 3961
10.3%
D 3865
10.0%
N 3815
9.9%
S 3494
 
9.1%
F 2778
 
7.2%
Dash Punctuation
ValueCountFrequency (%)
- 38478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115434
50.0%
Latin 115434
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10137
 
8.8%
J 9143
 
7.9%
u 8997
 
7.8%
a 8870
 
7.7%
c 7826
 
6.8%
p 6192
 
5.4%
n 6164
 
5.3%
A 5889
 
5.1%
M 5533
 
4.8%
r 5411
 
4.7%
Other values (12) 41272
35.8%
Common
ValueCountFrequency (%)
- 38478
33.3%
9 22596
19.6%
0 18766
16.3%
8 8151
 
7.1%
7 4652
 
4.0%
4 4127
 
3.6%
6 4124
 
3.6%
5 4087
 
3.5%
3 3656
 
3.2%
1 3610
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 38478
16.7%
9 22596
 
9.8%
0 18766
 
8.1%
e 10137
 
4.4%
J 9143
 
4.0%
u 8997
 
3.9%
a 8870
 
3.8%
8 8151
 
3.5%
c 7826
 
3.4%
p 6192
 
2.7%
Other values (23) 91712
39.7%

inq_last_6mths
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0839701
Minimum0
Maximum33
Zeros17787
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:47.086437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5348241
Coefficient of variation (CV)1.4159285
Kurtosis32.588372
Mean1.0839701
Median Absolute Deviation (MAD)1
Skewness3.5358174
Sum41709
Variance2.355685
MonotonicityNot monotonic
2023-08-27T18:43:47.296199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 17787
46.2%
1 10149
26.4%
2 5452
 
14.2%
3 2878
 
7.5%
4 955
 
2.5%
5 551
 
1.4%
6 304
 
0.8%
7 165
 
0.4%
8 104
 
0.3%
9 45
 
0.1%
Other values (18) 88
 
0.2%
ValueCountFrequency (%)
0 17787
46.2%
1 10149
26.4%
2 5452
 
14.2%
3 2878
 
7.5%
4 955
 
2.5%
5 551
 
1.4%
6 304
 
0.8%
7 165
 
0.4%
8 104
 
0.3%
9 45
 
0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
25 1
 
< 0.1%
24 2
< 0.1%
20 1
 
< 0.1%
19 2
< 0.1%
18 4
< 0.1%

mths_since_last_delinq
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct93
Distinct (%)0.7%
Missing24362
Missing (%)63.3%
Infinite0
Infinite (%)0.0%
Mean35.012326
Minimum0
Maximum120
Zeros745
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:47.558789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median33
Q351
95-th percentile75
Maximum120
Range120
Interquartile range (IQR)34

Descriptive statistics

Standard deviation22.425701
Coefficient of variation (CV)0.64050874
Kurtosis-0.86678787
Mean35.012326
Median Absolute Deviation (MAD)17
Skewness0.29878963
Sum494234
Variance502.91207
MonotonicityNot monotonic
2023-08-27T18:43:47.828392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 745
 
1.9%
30 246
 
0.6%
19 242
 
0.6%
15 238
 
0.6%
24 235
 
0.6%
23 234
 
0.6%
18 232
 
0.6%
38 224
 
0.6%
20 223
 
0.6%
31 222
 
0.6%
Other values (83) 11275
29.3%
(Missing) 24362
63.3%
ValueCountFrequency (%)
0 745
1.9%
1 30
 
0.1%
2 106
 
0.3%
3 143
 
0.4%
4 153
 
0.4%
5 145
 
0.4%
6 202
 
0.5%
7 184
 
0.5%
8 169
 
0.4%
9 175
 
0.5%
ValueCountFrequency (%)
120 1
 
< 0.1%
115 1
 
< 0.1%
106 1
 
< 0.1%
103 1
 
< 0.1%
96 1
 
< 0.1%
95 1
 
< 0.1%
89 1
 
< 0.1%
86 2
 
< 0.1%
85 1
 
< 0.1%
83 5
< 0.1%

open_acc
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3432091
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:48.075105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median9
Q312
95-th percentile18
Maximum47
Range46
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4978809
Coefficient of variation (CV)0.48140642
Kurtosis1.8739804
Mean9.3432091
Median Absolute Deviation (MAD)3
Skewness1.0385054
Sum359508
Variance20.230932
MonotonicityNot monotonic
2023-08-27T18:43:48.316863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
7 3847
10.0%
8 3778
9.8%
6 3756
9.8%
9 3547
9.2%
5 3086
 
8.0%
10 3059
 
7.9%
11 2686
 
7.0%
4 2268
 
5.9%
12 2167
 
5.6%
13 1864
 
4.8%
Other values (33) 8420
21.9%
ValueCountFrequency (%)
1 34
 
0.1%
2 631
 
1.6%
3 1447
 
3.8%
4 2268
5.9%
5 3086
8.0%
6 3756
9.8%
7 3847
10.0%
8 3778
9.8%
9 3547
9.2%
10 3059
7.9%
ValueCountFrequency (%)
47 1
 
< 0.1%
44 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
39 1
 
< 0.1%
38 2
 
< 0.1%
37 1
 
< 0.1%
36 1
 
< 0.1%
35 2
 
< 0.1%
34 8
< 0.1%

pub_rec
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057903217
Minimum0
Maximum5
Zeros36339
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:48.512127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24571022
Coefficient of variation (CV)4.243464
Kurtosis27.978845
Mean0.057903217
Median Absolute Deviation (MAD)0
Skewness4.665772
Sum2228
Variance0.060373512
MonotonicityNot monotonic
2023-08-27T18:43:48.700945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 36339
94.4%
1 2068
 
5.4%
2 57
 
0.1%
3 11
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 36339
94.4%
1 2068
 
5.4%
2 57
 
0.1%
3 11
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 2
 
< 0.1%
3 11
 
< 0.1%
2 57
 
0.1%
1 2068
 
5.4%
0 36339
94.4%

revol_bal
Real number (ℝ)

ZEROS 

Distinct21470
Distinct (%)55.8%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14290.238
Minimum0
Maximum1207359
Zeros976
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:48.926462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300
Q13640
median8840
Q317266
95-th percentile44323.3
Maximum1207359
Range1207359
Interquartile range (IQR)13626

Descriptive statistics

Standard deviation21941.541
Coefficient of variation (CV)1.5354217
Kurtosis368.45607
Mean14290.238
Median Absolute Deviation (MAD)6096
Skewness11.258751
Sum5.498169 × 108
Variance4.8143124 × 108
MonotonicityNot monotonic
2023-08-27T18:43:49.202871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 976
 
2.5%
255 14
 
< 0.1%
298 14
 
< 0.1%
400 10
 
< 0.1%
52 10
 
< 0.1%
6 10
 
< 0.1%
1 10
 
< 0.1%
182 9
 
< 0.1%
2725 9
 
< 0.1%
346 9
 
< 0.1%
Other values (21460) 37404
97.2%
ValueCountFrequency (%)
0 976
2.5%
1 10
 
< 0.1%
2 6
 
< 0.1%
3 7
 
< 0.1%
4 3
 
< 0.1%
5 6
 
< 0.1%
6 10
 
< 0.1%
7 5
 
< 0.1%
8 4
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
1207359 1
< 0.1%
952013 1
< 0.1%
508961 1
< 0.1%
487589 1
< 0.1%
423189 1
< 0.1%
407794 1
< 0.1%
401941 1
< 0.1%
394107 1
< 0.1%
388892 1
< 0.1%
385489 1
< 0.1%
Distinct1107
Distinct (%)2.9%
Missing59
Missing (%)0.2%
Memory size300.7 KiB
2023-08-27T18:43:49.706900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.5226581
Min length2

Characters and Unicode

Total characters212175
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)0.3%

Sample

1st row21.30%
2nd row99.90%
3rd row47.20%
4th row0%
5th row0%
ValueCountFrequency (%)
0 949
 
2.5%
0.10 60
 
0.2%
0.20 60
 
0.2%
70.40 59
 
0.2%
35.30 57
 
0.1%
31.20 55
 
0.1%
76.60 55
 
0.1%
46.60 55
 
0.1%
37.60 55
 
0.1%
70.10 54
 
0.1%
Other values (1097) 36960
96.2%
2023-08-27T18:43:50.479621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 38435
18.1%
% 38419
18.1%
. 33725
15.9%
4 11720
 
5.5%
7 11656
 
5.5%
5 11595
 
5.5%
6 11581
 
5.5%
3 11499
 
5.4%
2 11199
 
5.3%
8 11080
 
5.2%
Other values (2) 21266
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140031
66.0%
Other Punctuation 72144
34.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38435
27.4%
4 11720
 
8.4%
7 11656
 
8.3%
5 11595
 
8.3%
6 11581
 
8.3%
3 11499
 
8.2%
2 11199
 
8.0%
8 11080
 
7.9%
1 10725
 
7.7%
9 10541
 
7.5%
Other Punctuation
ValueCountFrequency (%)
% 38419
53.3%
. 33725
46.7%

Most occurring scripts

ValueCountFrequency (%)
Common 212175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38435
18.1%
% 38419
18.1%
. 33725
15.9%
4 11720
 
5.5%
7 11656
 
5.5%
5 11595
 
5.5%
6 11581
 
5.5%
3 11499
 
5.4%
2 11199
 
5.3%
8 11080
 
5.2%
Other values (2) 21266
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 212175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38435
18.1%
% 38419
18.1%
. 33725
15.9%
4 11720
 
5.5%
7 11656
 
5.5%
5 11595
 
5.5%
6 11581
 
5.5%
3 11499
 
5.4%
2 11199
 
5.3%
8 11080
 
5.2%
Other values (2) 21266
10.0%

total_acc
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.109049
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:50.803246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q113
median20
Q329
95-th percentile44
Maximum90
Range89
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.588253
Coefficient of variation (CV)0.52414073
Kurtosis0.65524708
Mean22.109049
Median Absolute Deviation (MAD)8
Skewness0.82306414
Sum850712
Variance134.28761
MonotonicityNot monotonic
2023-08-27T18:43:51.064689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 1423
 
3.7%
16 1408
 
3.7%
14 1387
 
3.6%
17 1385
 
3.6%
18 1362
 
3.5%
20 1351
 
3.5%
13 1342
 
3.5%
21 1335
 
3.5%
12 1286
 
3.3%
19 1276
 
3.3%
Other values (71) 24923
64.8%
ValueCountFrequency (%)
1 19
 
< 0.1%
2 34
 
0.1%
3 220
 
0.6%
4 438
 
1.1%
5 571
1.5%
6 691
1.8%
7 814
2.1%
8 967
2.5%
9 1032
2.7%
10 1142
3.0%
ValueCountFrequency (%)
90 1
 
< 0.1%
87 1
 
< 0.1%
80 1
 
< 0.1%
79 2
< 0.1%
77 1
 
< 0.1%
76 2
< 0.1%
75 2
< 0.1%
74 1
 
< 0.1%
73 3
< 0.1%
72 1
 
< 0.1%

total_pymnt
Real number (ℝ)

HIGH CORRELATION 

Distinct36820
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11981.008
Minimum0
Maximum58563.68
Zeros20
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:51.304326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1803.9849
Q15463.1711
median9673.6349
Q316402.677
95-th percentile30074.861
Maximum58563.68
Range58563.68
Interquartile range (IQR)10939.506

Descriptive statistics

Standard deviation9006.4151
Coefficient of variation (CV)0.75172431
Kurtosis2.0132894
Mean11981.008
Median Absolute Deviation (MAD)4976.349
Skewness1.3476654
Sum4.6100524 × 108
Variance81115514
MonotonicityNot monotonic
2023-08-27T18:43:51.576127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11196.56943 21
 
0.1%
0 20
 
0.1%
11784.23223 16
 
< 0.1%
10956.77596 15
 
< 0.1%
13148.13786 14
 
< 0.1%
5478.387981 14
 
< 0.1%
5557.025543 13
 
< 0.1%
13435.90021 12
 
< 0.1%
13263.95464 12
 
< 0.1%
4343.013754 10
 
< 0.1%
Other values (36810) 38331
99.6%
ValueCountFrequency (%)
0 20
0.1%
35.71 1
 
< 0.1%
44.92 2
 
< 0.1%
44.96 1
 
< 0.1%
57.18 1
 
< 0.1%
61.71 1
 
< 0.1%
66.77 1
 
< 0.1%
67.32 1
 
< 0.1%
69.64 1
 
< 0.1%
69.77 1
 
< 0.1%
ValueCountFrequency (%)
58563.67993 1
< 0.1%
58480.13992 1
< 0.1%
57835.27991 1
< 0.1%
56849.26986 1
< 0.1%
56199.43995 1
< 0.1%
55906.9499 1
< 0.1%
55768.77995 1
< 0.1%
55138.99996 1
< 0.1%
55106.27994 1
< 0.1%
54774.41992 1
< 0.1%

total_pymnt_inv
Real number (ℝ)

HIGH CORRELATION 

Distinct36374
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11274.813
Minimum0
Maximum58563.68
Zeros261
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:51.836000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1169.3535
Q14811.98
median8954.01
Q315486.997
95-th percentile29440.615
Maximum58563.68
Range58563.68
Interquartile range (IQR)10675.017

Descriptive statistics

Standard deviation8946.1616
Coefficient of variation (CV)0.79346432
Kurtosis2.0559067
Mean11274.813
Median Absolute Deviation (MAD)4960.4
Skewness1.3624561
Sum4.3383224 × 108
Variance80033806
MonotonicityNot monotonic
2023-08-27T18:43:52.092772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 261
 
0.7%
6514.52 14
 
< 0.1%
5478.39 13
 
< 0.1%
13148.14 13
 
< 0.1%
10956.78 12
 
< 0.1%
6717.95 12
 
< 0.1%
5557.03 11
 
< 0.1%
13517.36 11
 
< 0.1%
11784.23 10
 
< 0.1%
7328.92 10
 
< 0.1%
Other values (36364) 38111
99.0%
ValueCountFrequency (%)
0 261
0.7%
0.51 1
 
< 0.1%
0.54 1
 
< 0.1%
0.92 1
 
< 0.1%
11.21 1
 
< 0.1%
12.65 1
 
< 0.1%
16.82 1
 
< 0.1%
18.97 1
 
< 0.1%
20.98 1
 
< 0.1%
21.6 1
 
< 0.1%
ValueCountFrequency (%)
58563.68 1
< 0.1%
58438.37 1
< 0.1%
57628.73 1
< 0.1%
56515.16 1
< 0.1%
55867.02 1
< 0.1%
55579.28 1
< 0.1%
55066.92 1
< 0.1%
54675.68 1
< 0.1%
54432.79 1
< 0.1%
54241.58 1
< 0.1%

total_rec_prncp
Real number (ℝ)

HIGH CORRELATION 

Distinct7907
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9646.6634
Minimum0
Maximum35000.02
Zeros74
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:52.355169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1203.308
Q14400
median8000
Q313317.515
95-th percentile24250
Maximum35000.02
Range35000.02
Interquartile range (IQR)8917.515

Descriptive statistics

Standard deviation7051.7485
Coefficient of variation (CV)0.73100389
Kurtosis1.1031082
Mean9646.6634
Median Absolute Deviation (MAD)4000
Skewness1.1221576
Sum3.7118431 × 108
Variance49727156
MonotonicityNot monotonic
2023-08-27T18:43:52.610056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2181
 
5.7%
12000 1729
 
4.5%
5000 1650
 
4.3%
6000 1565
 
4.1%
15000 1349
 
3.5%
8000 1281
 
3.3%
20000 1033
 
2.7%
4000 937
 
2.4%
3000 865
 
2.2%
7000 812
 
2.1%
Other values (7897) 25076
65.2%
ValueCountFrequency (%)
0 74
0.2%
21.21 1
 
< 0.1%
21.93 1
 
< 0.1%
23.68 1
 
< 0.1%
24.87 1
 
< 0.1%
30.32 1
 
< 0.1%
32.51 1
 
< 0.1%
34.5 1
 
< 0.1%
35.14 1
 
< 0.1%
35.8 1
 
< 0.1%
ValueCountFrequency (%)
35000.02 1
 
< 0.1%
35000.01 1
 
< 0.1%
35000 333
0.9%
34999.99 4
 
< 0.1%
34999.98 1
 
< 0.1%
34999.97 1
 
< 0.1%
34800 1
 
< 0.1%
34675 1
 
< 0.1%
34525 1
 
< 0.1%
34475.01 1
 
< 0.1%

total_rec_int
Real number (ℝ)

HIGH CORRELATION 

Distinct34249
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2232.8263
Minimum0
Maximum23611.1
Zeros71
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:52.889331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.617
Q1657.7325
median1335.09
Q32795.125
95-th percentile7419.006
Maximum23611.1
Range23611.1
Interquartile range (IQR)2137.3925

Descriptive statistics

Standard deviation2570.1855
Coefficient of variation (CV)1.1510907
Kurtosis10.012057
Mean2232.8263
Median Absolute Deviation (MAD)854.82
Skewness2.7014866
Sum85914689
Variance6605853.5
MonotonicityNot monotonic
2023-08-27T18:43:53.142105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
0.2%
1196.57 21
 
0.1%
1784.23 17
 
< 0.1%
514.52 17
 
< 0.1%
956.78 16
 
< 0.1%
717.95 16
 
< 0.1%
1148.14 16
 
< 0.1%
478.39 15
 
< 0.1%
1517.36 13
 
< 0.1%
557.03 13
 
< 0.1%
Other values (34239) 38263
99.4%
ValueCountFrequency (%)
0 71
0.2%
3.54 1
 
< 0.1%
6.22 1
 
< 0.1%
6.27 1
 
< 0.1%
7.19 1
 
< 0.1%
7.2 2
 
< 0.1%
8.06 1
 
< 0.1%
8.92 1
 
< 0.1%
9.34 1
 
< 0.1%
9.49 1
 
< 0.1%
ValueCountFrequency (%)
23611.1 1
< 0.1%
23563.68 1
< 0.1%
23480.14 1
< 0.1%
22835.28 1
< 0.1%
22816.85 1
< 0.1%
22710.04 1
< 0.1%
22709.2 1
< 0.1%
22708.9 1
< 0.1%
22703.37 1
< 0.1%
22574.73 1
< 0.1%
Distinct103
Distinct (%)0.3%
Missing71
Missing (%)0.2%
Memory size300.7 KiB
2023-08-27T18:43:53.502337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters230442
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJul-13
2nd rowNov-11
3rd rowMar-14
4th rowFeb-14
5th rowMay-13
ValueCountFrequency (%)
jun-16 1022
 
2.7%
mar-13 966
 
2.5%
dec-14 870
 
2.3%
may-13 856
 
2.2%
feb-13 808
 
2.1%
apr-13 806
 
2.1%
mar-12 801
 
2.1%
aug-12 798
 
2.1%
oct-12 781
 
2.0%
aug-14 758
 
2.0%
Other values (93) 29941
78.0%
2023-08-27T18:43:54.131007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 42586
18.5%
- 38407
16.7%
u 10078
 
4.4%
J 9872
 
4.3%
a 9730
 
4.2%
e 9481
 
4.1%
3 8883
 
3.9%
2 8463
 
3.7%
4 8438
 
3.7%
M 6815
 
3.0%
Other values (23) 77689
33.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76814
33.3%
Lowercase Letter 76814
33.3%
Dash Punctuation 38407
16.7%
Uppercase Letter 38407
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 10078
13.1%
a 9730
12.7%
e 9481
12.3%
n 6783
8.8%
r 6761
8.8%
c 6582
8.6%
p 6000
7.8%
t 3186
 
4.1%
b 3161
 
4.1%
y 3130
 
4.1%
Other values (4) 11922
15.5%
Decimal Number
ValueCountFrequency (%)
1 42586
55.4%
3 8883
 
11.6%
2 8463
 
11.0%
4 8438
 
11.0%
0 3237
 
4.2%
5 2264
 
2.9%
6 1887
 
2.5%
9 757
 
1.0%
8 297
 
0.4%
7 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
J 9872
25.7%
M 6815
17.7%
A 6197
16.1%
D 3396
 
8.8%
O 3186
 
8.3%
F 3161
 
8.2%
S 2924
 
7.6%
N 2856
 
7.4%
Dash Punctuation
ValueCountFrequency (%)
- 38407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115221
50.0%
Latin 115221
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 10078
 
8.7%
J 9872
 
8.6%
a 9730
 
8.4%
e 9481
 
8.2%
M 6815
 
5.9%
n 6783
 
5.9%
r 6761
 
5.9%
c 6582
 
5.7%
A 6197
 
5.4%
p 6000
 
5.2%
Other values (12) 36922
32.0%
Common
ValueCountFrequency (%)
1 42586
37.0%
- 38407
33.3%
3 8883
 
7.7%
2 8463
 
7.3%
4 8438
 
7.3%
0 3237
 
2.8%
5 2264
 
2.0%
6 1887
 
1.6%
9 757
 
0.7%
8 297
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 42586
18.5%
- 38407
16.7%
u 10078
 
4.4%
J 9872
 
4.3%
a 9730
 
4.2%
e 9481
 
4.1%
3 8883
 
3.9%
2 8463
 
3.7%
4 8438
 
3.7%
M 6815
 
3.0%
Other values (23) 77689
33.7%

last_pymnt_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct33868
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2614.5097
Minimum0
Maximum36115.2
Zeros77
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size300.7 KiB
2023-08-27T18:43:54.428021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.8855
Q1212.0325
median526.005
Q33169.9625
95-th percentile12064.072
Maximum36115.2
Range36115.2
Interquartile range (IQR)2957.93

Descriptive statistics

Standard deviation4392.0064
Coefficient of variation (CV)1.6798585
Kurtosis9.1182667
Mean2614.5097
Median Absolute Deviation (MAD)430.445
Skewness2.7470832
Sum1.006011 × 108
Variance19289720
MonotonicityNot monotonic
2023-08-27T18:43:54.896268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77
 
0.2%
276.06 17
 
< 0.1%
200 17
 
< 0.1%
50 16
 
< 0.1%
100 15
 
< 0.1%
150 13
 
< 0.1%
500 11
 
< 0.1%
773.44 10
 
< 0.1%
400 9
 
< 0.1%
280.91 9
 
< 0.1%
Other values (33858) 38284
99.5%
ValueCountFrequency (%)
0 77
0.2%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.13 1
 
< 0.1%
0.16 1
 
< 0.1%
0.2 1
 
< 0.1%
0.24 1
 
< 0.1%
0.25 1
 
< 0.1%
0.28 1
 
< 0.1%
ValueCountFrequency (%)
36115.2 1
< 0.1%
35613.68 1
< 0.1%
35596.41 1
< 0.1%
35479.89 1
< 0.1%
35471.86 1
< 0.1%
35395.59 1
< 0.1%
35339.05 1
< 0.1%
35337.09 1
< 0.1%
35322.96 1
< 0.1%
35322.6 1
< 0.1%

next_pymnt_d
Text

MISSING 

Distinct101
Distinct (%)3.0%
Missing35097
Missing (%)91.2%
Memory size300.7 KiB
2023-08-27T18:43:55.262853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters20286
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st rowAug-13
2nd rowJun-13
3rd rowSep-10
4th rowJan-12
5th rowJul-11
ValueCountFrequency (%)
jul-16 909
26.9%
mar-11 100
 
3.0%
apr-11 87
 
2.6%
feb-11 85
 
2.5%
jan-11 72
 
2.1%
dec-10 67
 
2.0%
may-11 65
 
1.9%
sep-11 59
 
1.7%
jun-11 58
 
1.7%
nov-10 51
 
1.5%
Other values (91) 1828
54.1%
2023-08-27T18:43:55.868958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3744
18.5%
- 3381
16.7%
J 1444
 
7.1%
u 1436
 
7.1%
l 1070
 
5.3%
6 919
 
4.5%
0 840
 
4.1%
a 651
 
3.2%
e 635
 
3.1%
c 494
 
2.4%
Other values (23) 5672
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6762
33.3%
Lowercase Letter 6762
33.3%
Dash Punctuation 3381
16.7%
Uppercase Letter 3381
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 1436
21.2%
l 1070
15.8%
a 651
9.6%
e 635
9.4%
c 494
 
7.3%
r 465
 
6.9%
p 398
 
5.9%
n 374
 
5.5%
t 232
 
3.4%
v 209
 
3.1%
Other values (4) 798
11.8%
Decimal Number
ValueCountFrequency (%)
1 3744
55.4%
6 919
 
13.6%
0 840
 
12.4%
2 423
 
6.3%
3 335
 
5.0%
9 285
 
4.2%
8 90
 
1.3%
5 70
 
1.0%
4 55
 
0.8%
7 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
J 1444
42.7%
M 452
 
13.4%
A 409
 
12.1%
D 262
 
7.7%
O 232
 
6.9%
N 209
 
6.2%
F 193
 
5.7%
S 180
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 3381
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10143
50.0%
Latin 10143
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 1444
14.2%
u 1436
14.2%
l 1070
 
10.5%
a 651
 
6.4%
e 635
 
6.3%
c 494
 
4.9%
r 465
 
4.6%
M 452
 
4.5%
A 409
 
4.0%
p 398
 
3.9%
Other values (12) 2689
26.5%
Common
ValueCountFrequency (%)
1 3744
36.9%
- 3381
33.3%
6 919
 
9.1%
0 840
 
8.3%
2 423
 
4.2%
3 335
 
3.3%
9 285
 
2.8%
8 90
 
0.9%
5 70
 
0.7%
4 55
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3744
18.5%
- 3381
16.7%
J 1444
 
7.1%
u 1436
 
7.1%
l 1070
 
5.3%
6 919
 
4.5%
0 840
 
4.1%
a 651
 
3.2%
e 635
 
3.1%
c 494
 
2.4%
Other values (23) 5672
28.0%
Distinct108
Distinct (%)0.3%
Missing3
Missing (%)< 0.1%
Memory size300.7 KiB
2023-08-27T18:43:56.267893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters230850
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowJun-16
2nd rowMar-12
3rd rowMar-14
4th rowJun-16
5th rowJun-16
ValueCountFrequency (%)
jun-16 11342
29.5%
mar-16 1015
 
2.6%
apr-16 816
 
2.1%
feb-13 794
 
2.1%
may-16 695
 
1.8%
feb-16 664
 
1.7%
dec-15 602
 
1.6%
jan-16 591
 
1.5%
mar-13 564
 
1.5%
mar-14 522
 
1.4%
Other values (98) 20870
54.2%
2023-08-27T18:43:56.860780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 40355
17.5%
- 38475
16.7%
J 16922
 
7.3%
u 16904
 
7.3%
6 15123
 
6.6%
n 14974
 
6.5%
a 7638
 
3.3%
e 7274
 
3.2%
r 5757
 
2.5%
4 5726
 
2.5%
Other values (23) 61702
26.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76950
33.3%
Lowercase Letter 76950
33.3%
Dash Punctuation 38475
16.7%
Uppercase Letter 38475
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 16904
22.0%
n 14974
19.5%
a 7638
9.9%
e 7274
9.5%
r 5757
 
7.5%
p 4644
 
6.0%
c 4293
 
5.6%
b 2898
 
3.8%
y 2520
 
3.3%
o 2098
 
2.7%
Other values (4) 7950
10.3%
Decimal Number
ValueCountFrequency (%)
1 40355
52.4%
6 15123
 
19.7%
4 5726
 
7.4%
5 5084
 
6.6%
3 4875
 
6.3%
2 3882
 
5.0%
0 1456
 
1.9%
9 351
 
0.5%
8 56
 
0.1%
7 42
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
J 16922
44.0%
M 5675
 
14.7%
A 4547
 
11.8%
F 2898
 
7.5%
D 2334
 
6.1%
N 2098
 
5.5%
S 2042
 
5.3%
O 1959
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 38475
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115425
50.0%
Latin 115425
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 16922
14.7%
u 16904
14.6%
n 14974
13.0%
a 7638
 
6.6%
e 7274
 
6.3%
r 5757
 
5.0%
M 5675
 
4.9%
p 4644
 
4.0%
A 4547
 
3.9%
c 4293
 
3.7%
Other values (12) 26797
23.2%
Common
ValueCountFrequency (%)
1 40355
35.0%
- 38475
33.3%
6 15123
 
13.1%
4 5726
 
5.0%
5 5084
 
4.4%
3 4875
 
4.2%
2 3882
 
3.4%
0 1456
 
1.3%
9 351
 
0.3%
8 56
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 40355
17.5%
- 38475
16.7%
J 16922
 
7.3%
u 16904
 
7.3%
6 15123
 
6.6%
n 14974
 
6.5%
a 7638
 
3.3%
e 7274
 
3.2%
r 5757
 
2.5%
4 5726
 
2.5%
Other values (23) 61702
26.7%

repay_fail
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size300.7 KiB
0
32650 
1
5828 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38478
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32650
84.9%
1 5828
 
15.1%

Length

2023-08-27T18:43:57.136441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-27T18:43:57.360727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 32650
84.9%
1 5828
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 32650
84.9%
1 5828
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38478
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32650
84.9%
1 5828
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common 38478
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32650
84.9%
1 5828
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32650
84.9%
1 5828
 
15.1%

Interactions

2023-08-27T18:43:28.935474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:09.584575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:14.467867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:19.276476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:24.104422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:28.736102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:33.184698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:37.476559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:41.687978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:45.903542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:50.352074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:54.637135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:58.761585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:03.071193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:07.404703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:11.520151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:15.903166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:20.420128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:24.808452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:29.186766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:09.935282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:14.733492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:19.640071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:24.386922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:28.968209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:33.419158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:37.703194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:41.920546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:46.120059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:50.586707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:54.858817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:58.994955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:03.303692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:07.621400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:11.753585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:16.135820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:20.655719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:25.019998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:29.426405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:10.219434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:14.983498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:19.938945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:24.652636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:29.202810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:33.653762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:37.920063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:42.158869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:46.352295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:50.820115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:55.087941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:59.225343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:03.539915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:07.835793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:12.004784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:16.386495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:20.892204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:25.255598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:29.673260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:10.502962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:15.234883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:20.188893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:24.920239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:29.440078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:33.870107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:38.159583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:42.387170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:46.586728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:51.053495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:55.310600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:59.457612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:03.786049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:08.077330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:12.238887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:16.624168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:21.127428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:25.470022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:29.920284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:10.802807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:15.484880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:20.572329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:25.173130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:29.686704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:34.119126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:38.392984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:42.661841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:47.038815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:51.303417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:55.541799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:59.692159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:04.019930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:08.305417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:12.492144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:17.034938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:21.370046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:25.705822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:30.160410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:11.053154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:15.734876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:20.803019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:25.421393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:29.912606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:34.371365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:38.616159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:42.887164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:47.253263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:51.538173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:55.752040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:59.908663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:04.260580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:08.519078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:12.720180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:17.261488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:21.613794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:25.934371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:30.403672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:11.319225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:15.969250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:21.036789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:25.653634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:30.142027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:34.588866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:38.836568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:43.105559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:47.470269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:51.752554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:55.953480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:00.118789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:04.493153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:08.735334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:12.951520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:17.492085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:21.841176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:26.151990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:30.637041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:11.547450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:16.221242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:21.285927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:25.905605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:30.358488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:34.811591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:39.058869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:43.318508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:47.694461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:51.987126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:56.176429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:00.344925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:04.720197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-08-27T18:42:28.495472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:32.786681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:37.241931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:41.454680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:45.677445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:50.128422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:54.386604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:42:58.520136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:02.836310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:07.151939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:11.306795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:15.672110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:20.188098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:24.553193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-27T18:43:28.719391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-27T18:43:57.577922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
loan_amntfunded_amntfunded_amnt_invint_rateinstallmentannual_incdtidelinq_2yrsinq_last_6mthsmths_since_last_delinqopen_accpub_recrevol_baltotal_acctotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_intlast_pymnt_amnttermemp_lengthhome_ownershipverification_statusloan_statuspurposeaddr_staterepay_fail
loan_amnt1.0000.9910.9170.2330.9590.4350.071-0.035-0.0270.0290.211-0.0500.4000.2780.8810.8210.8310.7750.4530.3560.0550.0900.3080.0620.1170.0210.049
funded_amnt0.9911.0000.9280.2400.9730.4310.071-0.036-0.0270.0310.208-0.0510.3950.2730.8900.8310.8400.7830.4570.3370.0550.0880.3020.0600.1160.0180.046
funded_amnt_inv0.9170.9281.0000.2190.8890.4010.077-0.048-0.0640.1400.189-0.0550.3610.2630.8460.9070.7990.7420.4450.3680.0610.0860.3160.0860.1070.0330.046
int_rate0.2330.2400.2191.0000.2340.0590.1260.1630.205-0.041-0.0130.1020.133-0.0580.2060.1880.0980.4890.0650.4580.0240.0520.1580.1070.0590.0140.199
installment0.9590.9730.8890.2341.0000.4290.065-0.022-0.0150.0140.202-0.0450.3970.2530.8650.7960.8280.7340.4420.1440.0450.0730.2660.0370.1140.0180.031
annual_inc0.4350.4310.4010.0590.4291.000-0.0910.0350.0240.0190.314-0.0140.4060.4370.4080.3820.4000.3110.2460.0000.0030.0000.0060.0000.0000.0150.000
dti0.0710.0710.0770.1260.065-0.0911.000-0.0390.0190.0640.3150.0010.3370.2500.0600.0650.0380.1200.0110.0770.0180.0210.0720.0300.0820.0340.044
delinq_2yrs-0.035-0.036-0.0480.163-0.0220.035-0.0391.0000.020-0.6720.0090.006-0.0820.073-0.028-0.041-0.0420.021-0.0220.0000.0000.0090.0000.0120.0140.0140.019
inq_last_6mths-0.027-0.027-0.0640.205-0.0150.0240.0190.0201.000-0.0300.0940.053-0.0310.094-0.058-0.086-0.0750.005-0.0180.0390.0110.0210.0500.2500.0310.0880.087
mths_since_last_delinq0.0290.0310.140-0.0410.0140.0190.064-0.672-0.0301.0000.0430.0590.0800.0420.0370.1320.0390.0250.0420.0700.0400.0250.0860.0580.0110.0510.038
open_acc0.2110.2080.189-0.0130.2020.3140.3150.0090.0940.0431.0000.0080.4040.6960.1880.1710.1830.1530.1000.0510.0360.1110.0720.0280.0500.0220.020
pub_rec-0.050-0.051-0.0550.102-0.045-0.0140.0010.0060.0530.0590.0081.000-0.053-0.004-0.058-0.060-0.069-0.002-0.0380.0000.0280.0110.0060.0330.0150.0370.058
revol_bal0.4000.3950.3610.1330.3970.4060.337-0.082-0.0310.0800.404-0.0531.0000.3920.3570.3270.3330.3410.1630.0190.0140.0350.0210.0800.0210.0280.016
total_acc0.2780.2730.263-0.0580.2530.4370.2500.0730.0940.0420.696-0.0040.3921.0000.2430.2350.2430.1620.1780.0980.0760.1710.1000.0300.0530.0350.033
total_pymnt0.8810.8900.8460.2060.8650.4080.060-0.028-0.0580.0370.188-0.0580.3570.2431.0000.9430.9770.8230.4990.3320.0480.0780.2790.1270.1000.0180.301
total_pymnt_inv0.8210.8310.9070.1880.7960.3820.065-0.041-0.0860.1320.171-0.0600.3270.2350.9431.0000.9210.7770.4840.3520.0540.0780.2860.1330.0950.0230.293
total_rec_prncp0.8310.8400.7990.0980.8280.4000.038-0.042-0.0750.0390.183-0.0690.3330.2430.9770.9211.0000.7230.5390.2510.0470.0810.2670.1710.1010.0210.454
total_rec_int0.7750.7830.7420.4890.7340.3110.1200.0210.0050.0250.153-0.0020.3410.1620.8230.7770.7231.0000.2490.5340.0420.0590.2520.1200.0690.0070.009
last_pymnt_amnt0.4530.4570.4450.0650.4420.2460.011-0.022-0.0180.0420.100-0.0380.1630.1780.4990.4840.5390.2491.0000.2350.0280.0520.1480.0870.0450.0000.227
term0.3560.3370.3680.4580.1440.0000.0770.0000.0390.0700.0510.0000.0190.0980.3320.3520.2510.5340.2351.0000.1190.1090.2670.3190.1140.0510.134
emp_length0.0550.0550.0610.0240.0450.0030.0180.0000.0110.0400.0360.0280.0140.0760.0480.0540.0470.0420.0280.1191.0000.1310.0870.0300.0370.0210.014
home_ownership0.0900.0880.0860.0520.0730.0000.0210.0090.0210.0250.1110.0110.0350.1710.0780.0780.0810.0590.0520.1090.1311.0000.0740.0350.1230.1320.023
verification_status0.3080.3020.3160.1580.2660.0060.0720.0000.0500.0860.0720.0060.0210.1000.2790.2860.2670.2520.1480.2670.0870.0741.0000.1080.0960.0430.031
loan_status0.0620.0600.0860.1070.0370.0000.0300.0120.2500.0580.0280.0330.0800.0300.1270.1330.1710.1200.0870.3190.0300.0350.1081.0000.0460.0531.000
purpose0.1170.1160.1070.0590.1140.0000.0820.0140.0310.0110.0500.0150.0210.0530.1000.0950.1010.0690.0450.1140.0370.1230.0960.0461.0000.0330.098
addr_state0.0210.0180.0330.0140.0180.0150.0340.0140.0880.0510.0220.0370.0280.0350.0180.0230.0210.0070.0000.0510.0210.1320.0430.0530.0331.0000.056
repay_fail0.0490.0460.0460.1990.0310.0000.0440.0190.0870.0380.0200.0580.0160.0330.3010.2930.4540.0090.2270.1340.0140.0230.0311.0000.0980.0561.000

Missing values

2023-08-27T18:43:33.934237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-27T18:43:35.097483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-27T18:43:36.160404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

loan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspurposezip_codeaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsmths_since_last_delinqopen_accpub_recrevol_balrevol_utiltotal_acctotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_intlast_pymnt_dlast_pymnt_amntnext_pymnt_dlast_credit_pull_drepay_fail
02500.02500.02500.0000036 months13.9885.424 yearsRENT20004.0Not VerifiedJul-10Does not meet the credit policy. Status:Fully Paidother487xxMI19.860.0Aug-055.0NaN7.00.0981.021.30%10.03075.2917793075.292500.00575.29Jul-1390.85Aug-13Jun-160
15000.05000.05000.0000036 months15.95175.674 yearsRENT59000.0Not VerifiedJun-10Charged Offdebt_consolidation115xxNY19.570.0Apr-941.059.07.00.018773.099.90%15.02948.7600002948.761909.02873.81Nov-11175.67NaNMar-121
27000.07000.07000.0000036 months9.91225.5810+ yearsMORTGAGE53796.0Not VerifiedSep-11Fully Paidother751xxTX10.803.0Mar-983.03.07.00.03269.047.20%20.08082.3918808082.397000.001082.39Mar-141550.27NaNMar-140
32000.02000.02000.0000036 months5.4260.3210+ yearsRENT30000.0Not VerifiedSep-11Fully Paiddebt_consolidation112xxNY3.600.0Jan-750.072.07.00.00.00%15.02161.6632442161.662000.00161.66Feb-1453.12NaNJun-160
43600.03600.03600.0000036 months10.25116.5910+ yearsMORTGAGE675048.0Not VerifiedApr-10Does not meet the credit policy. Status:Fully Paidother352xxAL1.550.0Apr-984.025.08.00.00.00%25.04206.0311914206.033600.00606.03May-13146.75Jun-13Jun-160
58000.08000.08000.0000036 months6.03243.49NaNMORTGAGE77736.0VerifiedOct-11Fully Paidother853xxAZ6.070.0Jul-960.0NaN12.00.04182.013.60%49.08724.9718158724.978000.00724.97Apr-141423.66NaNApr-140
66000.06000.06000.0000036 months7.49186.613 yearsRENT35000.0Not VerifiedMay-11Fully Paiddebt_consolidation658xxMO13.130.0Oct-030.0NaN5.00.05864.047.70%9.06717.9501096717.956000.00717.95May-14211.41NaNMay-140
725600.025600.025472.8294760 months14.27599.264 yearsRENT86000.0VerifiedNov-11Fully Paiddebt_consolidation105xxNY26.510.0Oct-831.0NaN16.00.033021.070.80%32.032840.05674032659.1325600.007240.06Apr-1416083.78NaNJun-160
819750.019750.019750.0000060 months23.22559.2710+ yearsMORTGAGE72500.0VerifiedJun-11Fully Paiddebt_consolidation630xxMO19.960.0Mar-920.061.015.00.021544.098.70%44.027544.89116027544.8919750.007794.89Jun-1315264.34NaNFeb-160
96250.06250.06250.0000036 months17.27223.684 yearsMORTGAGE28000.0VerifiedNov-11Charged Offother450xxOH13.760.0Oct-990.041.02.01.00.08.46%15.06688.6400006688.644764.181713.09Apr-14223.68NaNSep-141
loan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspurposezip_codeaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsmths_since_last_delinqopen_accpub_recrevol_balrevol_utiltotal_acctotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_intlast_pymnt_dlast_pymnt_amntnext_pymnt_dlast_credit_pull_drepay_fail
384684000.04000.04000.0000036 months14.27137.246 yearsMORTGAGE50000.0Not VerifiedNov-11Fully Paidcredit_card330xxFL19.201.0Aug-960.023.023.00.017561.063.60%43.04940.4500004940.454000.00940.45Nov-14149.24NaNNov-140
384691500.01500.01500.0000036 months16.2952.961 yearRENT27000.0Not VerifiedOct-11Charged Offwedding301xxGA18.530.0Sep-072.0NaN9.00.0743.026.50%12.086.05000086.050.000.00NaN0.00NaNApr-121
384707200.07200.07200.0000036 months15.05249.784 yearsOTHER75000.0Source VerifiedFeb-09Fully Paiddebt_consolidation021xxMA8.001.0Dec-960.018.04.00.017321.087.60%11.08991.9393418991.947200.001791.94Mar-12251.68NaNMar-120
384716000.06000.00.0000036 months12.92201.941 yearRENT32000.0Not VerifiedJun-08Fully Paidcar210xxMD4.760.0Sep-040.0NaN7.00.04378.071.20%7.07023.8352970.006000.001023.84Mar-102985.40NaNApr-150
384726925.06925.06900.0000036 months9.62222.22< 1 yearRENT78000.0VerifiedDec-10Charged Offdebt_consolidation031xxNH15.400.0Nov-032.043.017.00.07297.034.30%22.06108.7900006086.774994.45995.23Mar-13222.22NaNAug-131
384733000.03000.03000.0000036 months11.9999.633 yearsRENT74250.0VerifiedAug-11Fully Paidcredit_card805xxCO22.170.0Jan-002.0NaN12.00.016550.073.50%22.03586.6197643586.623000.00586.62Aug-14100.30NaNJun-160
3847410400.010400.010400.0000036 months13.49352.889 yearsRENT62000.0Source VerifiedAug-11Fully Paidcredit_card442xxOH11.570.0Apr-991.0NaN21.00.016898.039.70%33.012703.53403012703.5310400.002303.53Sep-14393.08NaNJun-160
3847516000.010550.010531.3581860 months14.96250.7710+ yearsMORTGAGE95088.0Source VerifiedMay-10Fully Paiddebt_consolidation073xxNJ10.630.0Dec-951.0NaN9.01.010386.062.20%25.014202.26753014163.3110550.003652.27Jun-135439.96NaNFeb-160
3847610000.010000.010000.0000036 months16.89355.991 yearRENT48720.0Not VerifiedJul-11Fully Paiddebt_consolidation061xxCT18.970.0Jul-072.0NaN8.00.07301.053.30%12.012815.17832012815.1810000.002815.18Aug-14380.63NaNSep-150
384773200.03200.03200.0000036 months13.49108.587 yearsRENT38400.0Source VerifiedNov-11Fully Paiddebt_consolidation484xxMI12.560.0Oct-952.038.08.01.02503.062.60%18.03908.7672753908.773200.00708.77Dec-14111.88NaNNov-140